Parkinson`s disease (PD) is a chronic disorder of the central nervous system, characterized by degeneration of dopaminergic neurons leading to progressive gait dysfunction. To maintain patient’s quality of life, objective classification of gait symptoms in PD is crucial to adequately manage the individual treatment. We will establish a sensor based biometric gait-analysis that enables reproducible, objective, rater-independent assessment of gait symptoms. With a therapist independent rating completely comparable results can be reached. Different sensors (gyroscopes, accelerometers, in-sole pressure sensors,...) attached to a comfortable sport shoe detected motion signals assessed during standardized exercises while the subject is walking or sitting on a chair. Using pattern recognition methods, signal features should be analyzed from PD patients and healthy controls. Classification between patients and controls and a identification of different PD stages should be done. A pilot study suggests that biometric gait-analysis may be an important and complementary mean to support disease management in PD. Future biometric studies will help to monitor the disease course, to modify and adjust treatment thus rationalizing therapeutic decisions. To differentiate between PD specific and age dependent gait disorders also data from subjects in different  decades of life should be analyzed.

www.egait.de

EFI-Moves

This project is a close cooperation between

• the Pattern Recognition Lab,

• the Department for Molecular Neurology of the University Hospital in Erlangen and

• the company ASTRUM IT GmbH.